Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set

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dc.authoridARSLAN, Ahmet Kadir/0000-0001-8626-9542
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridKIVRAK, MEHMET/0000-0002-2405-8552
dc.authorwosidARSLAN, Ahmet Kadir/AAA-2409-2020
dc.authorwosidAkbaş, Kübra Elif/V-9690-2018
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidKIVRAK, MEHMET/AAH-4386-2021
dc.contributor.authorAkbas, Kubra Elif
dc.contributor.authorKivrak, Mehmet
dc.contributor.authorArslan, A. Kadir
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:57:12Z
dc.date.available2024-08-04T20:57:12Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractAssociation rules mining is one of the uttermost applied techniques in data mining and artificial intelligence. Support and confidence are two basic measures employed in the evaluation of association rules. The rules obtained with these two values are often correct; however, they are not strong rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. In this study it is aimed to establish strong association rules with variables in open sourced heart data set. In the current study, Apriori algorithm was used to obtain the rules. As a result of the analysis, only 55 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measure proposed in the literature, and it was concluded that only 26 of these rules were strong. As a result of the analysis of the findings obtained in the context of the research, it can be inferred that stronger rules can be obtained by using the certainty factor in association rules mining.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Sci,Inonu Univ, Comp Sci Dept,Inonu Univ, Muhendisli Fakultesien_US
dc.identifier.doi10.1109/idap.2019.8875977
dc.identifier.urihttps://doi.org/10.1109/idap.2019.8875977
dc.identifier.urihttps://hdl.handle.net/11616/102410
dc.identifier.wosWOS:000591781100104en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 International Conference on Artificial Intelligence and Data Processing (Idap 2019)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData Miningen_US
dc.subjectAssociation Rules miningen_US
dc.subjectApriori Algorithmen_US
dc.subjectInterestingness Measuresen_US
dc.titleAssessment of Association Rules based on Certainty Factor: an Application on Heart Data Seten_US
dc.typeConference Objecten_US

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